1. Technological Field
The present disclosure relates to parameter estimation, object classification, data regression, detection, and feature detection using spiking neuron networks.
2. Background
Statistical methods may be used for classification and/or detection applications. One such method, a type of Bayes classifier with conditionally independent feature subsets (also referred as “ferns”) may utilize multiple sub-classifiers. Individual sub-classifiers may be used to classify subsets of a total feature set fn.
A classifier with conditionally independent subsets may make a direct tradeoff between the complexity of full Bayes classifier and the tractability of independent Bayes classifier.
Artificial spiking neural networks may be used in signal processing and/or for solving artificial intelligence problems. Such networks may employ a pulse-coded mechanism. The pulse-coded mechanism may encode information using the timing of the pulses (e.g., temporal pulse latency). Such pulses (also referred to as “spikes” or ‘impulses’) may be described as short-lasting (e.g., on the order of 1-2 ms) discrete temporal events. Several exemplary implementations of such encoding are described in commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 entitled APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, filed Jun. 2, 2011 and co-owned U.S. patent application Ser. No. 13/152,119, filed Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, and patented as U.S. Pat. No. 8,942,466 on Jan. 27, 2015, each of the foregoing being incorporated herein by reference in its entirety. Spiking neuron networks may be used to convert visual sensory input into spiking output for further processing.